Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning.
<h4>Background</h4>Novel and highly sensitive point-of-care malaria diagnostic and surveillance tools that are rapid and affordable are urgently needed to support malaria control and elimination.<h4>Methods</h4>We demonstrated the potential of near-infrared spectroscopy (NIRS...
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0289232&type=printable |
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| author | Maggy T Sikulu-Lord Michael D Edstein Brendon Goh Anton R Lord Jye A Travis Floyd E Dowell Geoffrey W Birrell Marina Chavchich |
| author_facet | Maggy T Sikulu-Lord Michael D Edstein Brendon Goh Anton R Lord Jye A Travis Floyd E Dowell Geoffrey W Birrell Marina Chavchich |
| author_sort | Maggy T Sikulu-Lord |
| collection | DOAJ |
| description | <h4>Background</h4>Novel and highly sensitive point-of-care malaria diagnostic and surveillance tools that are rapid and affordable are urgently needed to support malaria control and elimination.<h4>Methods</h4>We demonstrated the potential of near-infrared spectroscopy (NIRS) technique to detect malaria parasites both, in vitro, using dilutions of infected red blood cells obtained from Plasmodium falciparum cultures and in vivo, in mice infected with P. berghei using blood spotted on slides and non-invasively, by simply scanning various body areas (e.g., feet, groin and ears). The spectra were analysed using machine learning to develop predictive models for infection.<h4>Findings</h4>Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low densities (<10-7 parasites/μL) of P. falciparum parasites with a sensitivity of 96% (n = 1041), a specificity of 93% (n = 130) and an accuracy of 96% (n = 1171) and differentiated ring, trophozoite and schizont stages with an accuracy of 98% (n = 820). Furthermore, when the feet of mice infected with P. berghei with parasitaemia ≥3% were scanned non-invasively, the sensitivity and specificity of NIRS were 94% (n = 66) and 86% (n = 342), respectively.<h4>Interpretation</h4>These data highlights the potential of NIRS technique as rapid, non-invasive and affordable tool for surveillance of malaria cases. Further work to determine the potential of NIRS to detect malaria in symptomatic and asymptomatic malaria cases in the field is recommended including its capacity to guide current malaria elimination strategies. |
| format | Article |
| id | doaj-art-aa74b64331e048b184cd0a4ab23a8f25 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-aa74b64331e048b184cd0a4ab23a8f252025-08-20T02:14:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-01193e028923210.1371/journal.pone.0289232Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning.Maggy T Sikulu-LordMichael D EdsteinBrendon GohAnton R LordJye A TravisFloyd E DowellGeoffrey W BirrellMarina Chavchich<h4>Background</h4>Novel and highly sensitive point-of-care malaria diagnostic and surveillance tools that are rapid and affordable are urgently needed to support malaria control and elimination.<h4>Methods</h4>We demonstrated the potential of near-infrared spectroscopy (NIRS) technique to detect malaria parasites both, in vitro, using dilutions of infected red blood cells obtained from Plasmodium falciparum cultures and in vivo, in mice infected with P. berghei using blood spotted on slides and non-invasively, by simply scanning various body areas (e.g., feet, groin and ears). The spectra were analysed using machine learning to develop predictive models for infection.<h4>Findings</h4>Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low densities (<10-7 parasites/μL) of P. falciparum parasites with a sensitivity of 96% (n = 1041), a specificity of 93% (n = 130) and an accuracy of 96% (n = 1171) and differentiated ring, trophozoite and schizont stages with an accuracy of 98% (n = 820). Furthermore, when the feet of mice infected with P. berghei with parasitaemia ≥3% were scanned non-invasively, the sensitivity and specificity of NIRS were 94% (n = 66) and 86% (n = 342), respectively.<h4>Interpretation</h4>These data highlights the potential of NIRS technique as rapid, non-invasive and affordable tool for surveillance of malaria cases. Further work to determine the potential of NIRS to detect malaria in symptomatic and asymptomatic malaria cases in the field is recommended including its capacity to guide current malaria elimination strategies.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0289232&type=printable |
| spellingShingle | Maggy T Sikulu-Lord Michael D Edstein Brendon Goh Anton R Lord Jye A Travis Floyd E Dowell Geoffrey W Birrell Marina Chavchich Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning. PLoS ONE |
| title | Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning. |
| title_full | Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning. |
| title_fullStr | Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning. |
| title_full_unstemmed | Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning. |
| title_short | Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning. |
| title_sort | rapid and non invasive detection of malaria parasites using near infrared spectroscopy and machine learning |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0289232&type=printable |
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